Life after death : techniques for the prognostication of coma outcomes after cardiac arrest
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/118092 |
| _version_ | 1811097851291238400 |
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| author | Ghassemi, Mohammad Mahdi |
| author2 | Roger G. Mark and Emery N. Brown. |
| author_facet | Roger G. Mark and Emery N. Brown. Ghassemi, Mohammad Mahdi |
| author_sort | Ghassemi, Mohammad Mahdi |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T17:05:57Z |
| format | Thesis |
| id | mit-1721.1/118092 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T17:05:57Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1180922019-04-10T14:49:29Z Life after death : techniques for the prognostication of coma outcomes after cardiac arrest Techniques for the prognostication of coma outcomes after cardiac arrest Ghassemi, Mohammad Mahdi Roger G. Mark and Emery N. Brown. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 120-134). Electroencephalography (EEG) features are known to predict neurological outcomes of patients in coma after cardiac arrest, but the association between EEG features and outcomes is time-dependent. Recent advances in machine learning allow temporally-dependent features to be learned from the EEG waveforms in a fully-automated way, allowing for faster, better-calibrated and more reliable prognostic predictions. In this thesis, we discuss three major contributions to the problem of coma prognostication after cardiac arrest: (1) the collection of the world's largest multi-center EEG database for patients in coma after cardiac arrest, (2) the development of time-dependent, interpretable, feature-based EEG models that may be used for both risk-scoring and decision support at the bedside, and (3) a careful comparison of the performance and utility of feature-based techniques to that of representation learning models that fully-automate the extraction of time-dependent features for outcome prognostication. by Mohammad Mahdi Ghassemi. Ph. D. 2018-09-17T15:57:10Z 2018-09-17T15:57:10Z 2018 2018 Thesis http://hdl.handle.net/1721.1/118092 1052124083 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 134 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ghassemi, Mohammad Mahdi Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title | Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title_full | Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title_fullStr | Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title_full_unstemmed | Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title_short | Life after death : techniques for the prognostication of coma outcomes after cardiac arrest |
| title_sort | life after death techniques for the prognostication of coma outcomes after cardiac arrest |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/118092 |
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